scBSP: A Fast Tool for Single-Cell Spatially Variable Genes Identifications on Large-Scale Data (original) (raw)
Identifying spatially variable genes is critical in linking molecular cell functions with tissue phenotypes. This package utilizes a granularity-based dimension-agnostic tool, single-cell big-small patch (scBSP), implementing sparse matrix operation and KD tree methods for distance calculation, for the identification of spatially variable genes on large-scale data. The detailed description of this method is available at Wang, J. and Li, J. et al. 2023 (Wang, J. and Li, J. (2023), <doi:10.1038/s41467-023-43256-5>).
Version: | 1.0.0 |
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Imports: | Matrix, sparseMatrixStats, fitdistrplus, RANN, spam |
Suggests: | knitr, rmarkdown |
Published: | 2024-05-03 |
DOI: | 10.32614/CRAN.package.scBSP |
Author: | Jinpu Li [aut, cre], Yiqing Wang [aut] |
Maintainer: | Jinpu Li <castle.lee.f at gmail.com> |
License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
NeedsCompilation: | no |
CRAN checks: | scBSP results |
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